Arctic picos
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require(iNEXT); packageVersion("iNEXT")## Loading required package: iNEXT## [1] '3.0.0'require(tidyverse); packageVersion("tidyverse")## Loading required package: tidyverse## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## This is vegan 2.6-4## [1] '2.6.4'require(tibble) #for "rownames_to_column('Site')"
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## smiths## [1] '1.4.4'source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/data_import/import.R", encoding = "UTF-8")
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/data_import/rename_metadata.R", encoding = "UTF-8")remove replicates and outliers
outliers arose from crysochromulina bloom (John et al. in prep) and replicates only available from HE533, thus we always used Rep. A from each site.
outliers_euks <- c("HE533.Euk.F02.04C_S9", "HE533.Euk.F02.08B_S20", "HE533.Euk.F02.08C_S21", "HE533.Euk.F02.09A_S22",
"HE533.Euk.F02.10A_S25", "HE533.Euk.F02.10C_S27", "HE533.Euk.F02.11A_S28", "HE533.Euk.F02.11C_S30",
"HE533.Euk.F02.12B_S32", "HE533.Euk.F02.12C_S33", "HE533.Euk.F02.19C_S54", "HE533.Euk.F02.22C_S63")
#remove replicates
rep_18S <- c("HE533.Euk.F02.25B_S68", "HE533.Euk.F02.25C_S69", "HE533.Euk.F02.26B_S71", "HE533.Euk.F02.26C_S72", "HE533.Euk.F02.27B_S74", "HE533.Euk.F02.27C_S75", "HE533.Euk.F02.28B_S77", "HE533.Euk.F02.28C_S78",
"HE533.Euk.F02.21B_S59", "HE533.Euk.F02.21C_S60", "HE533.Euk.F02.22B_S62", "HE533.Euk.F02.23B_S65", "HE533.Euk.F02.23C_S66", "HE533.Euk.F02.17B_S47", "HE533.Euk.F02.17C_S48", "HE533.Euk.F02.18B_S50", "HE533.Euk.F02.18C_S51", "HE533.Euk.F02.19B_S53", "HE533.Euk.F02.20B_S56", "HE533.Euk.F02.20C_S57",
"HE533.Euk.F02.13C_S36", "HE533.Euk.F02.14B_S38", "HE533.Euk.F02.14C_S39", "HE533.Euk.F02.15B_S41", "HE533.Euk.F02.15C_S42", "HE533.Euk.F02.16B_S44", "HE533.Euk.F02.16C_S45", "HE533.Euk.F02.07B_S17", "HE533.Euk.F02.07C_S18", "HE533.Euk.F02.09C_S24", "HE533.Euk.F02.04B_S8", "HE533.Euk.F02.05B_S11",
"HE533.Euk.F02.05C_S12", "HE533.Euk.F02.06B_S14", "HE533.Euk.F02.06C_S15", "HE533.Euk.F02.2B_S2", "HE533.Euk.F02.2C_S3", "HE533.Euk.F02.3B_S5", "HE533.Euk.F02.3C_S6")
rep_16S <- c("HE533.Prok.F02.25B_S90", "HE533.Prok.F02.25C_S91", "HE533.Prok.F02.26B_S94", "HE533.Prok.F02.26C_S95", "HE533.Prok.F02.27B_S98", "HE533.Prok.F02.27C_S99", "HE533.Prok.F02.28B_S102", "HE533.Prok.F02.28C_S103", "HE533.Prok.F02.21B_S78", "HE533.Prok.F02.21C_S79", "HE533.Prok.F02.22B_S82", "HE533.Prok.F02.22C_S83", "HE533.Prok.F02.23B_S86", "HE533.Prok.F02.23C_S87", "HE533.Prok.F02.17B_S62", "HE533.Prok.F02.17C_S63", "HE533.Prok.F02.18B_S66", "HE533.Prok.F02.18C_S67", "HE533.Prok.F02.19B_S70", "HE533.Prok.F02.19C_S71", "HE533.Prok.F02.20B_S74", "HE533.Prok.F02.20C_S75", "HE533.Prok.F02.11B_S38", "HE533.Prok.F02.11C_S39", "HE533.Prok.F02.12B_S42", "HE533.Prok.F02.12C_S43", "HE533.Prok.F02.13B_S46", "HE533.Prok.F02.13C_S47", "HE533.Prok.F02.14B_S50", "HE533.Prok.F02.14C_S51", "HE533.Prok.F02.15B_S54", "HE533.Prok.F02.15C_S55", "HE533.Prok.F02.16B_S58", "HE533.Prok.F02.16C_S59", "HE533.Prok.F02.10B_S22", "HE533.Prok.F02.7B_S34", "HE533.Prok.F02.7C_S23", "HE533.Prok.F02.8B_S26", "HE533.Prok.F02.8C_S27", "HE533.Prok.F02.2B_S2", "HE533.Prok.F02.2C_S3", "HE533.Prok.F02.3B_S6", "HE533.Prok.F02.3C_S7", "HE533.Prok.F02.4B_S10", "HE533.Prok.F02.4C_S11", "HE533.Prok.F02.5B_S14", "HE533.Prok.F02.5C_S15", "HE533.Prok.F02.6B_S18", "HE533.Prok.F02.6C_S19", "HE533.Prok.F02.9B_S30", "HE533.Prok.F02.9C_S31")
###remove outliers
euk_r <- eukaryotes%>%dplyr::select(!outliers_euks)
euk_r <- euk_r%>%dplyr::select(!rep_18S)
euk_r <- euk_r[rowSums(euk_r)>0,]
prok_r <- prokaryotes%>%dplyr::select(!rep_16S)
prok_r <- prok_r[rowSums(prok_r)>0,]
prokaryotes = prok_r
meta_18S_r <- meta_18S%>%dplyr::filter(Site %in% colnames(euk_r))
meta_16S <- meta_16S%>%dplyr::filter(Site %in% colnames(prokaryotes))
rm(outliers_euks, rep_16S, rep_18S)import the distance tables of drifters
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/env_calc/drifter_calc.R", encoding = "UTF-8")## Loading required package: BBmisc##
## Attaching package: 'BBmisc'## The following objects are masked from 'package:dplyr':
##
## coalesce, collapse## The following object is masked from 'package:base':
##
## isFALSElist_drifter <- sapply(ls(pattern="drifter_"), function(x) get(x), simplify = FALSE)
rm(list = ls(pattern="drifter_"))
rm(info)metadata removing NAs and z-scoring
## Joining, by = c("Station", "Glacial.influence", "Latitude", "Longitude")metadata exploration
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/env_calc/MLD_DK.R", encoding = "UTF-8")
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/env_calc/MLD_calculations.R", encoding = "UTF-8")add seasonality (based on sun altitude and azimuth) to the analysis
## Loading required package: suncalc## Joining, by = c("date", "lat", "lon")
## Loading required package: suncalc
## Joining, by = c("date", "lat", "lon")create list of metadata
meta_all <- dplyr::inner_join(meta_16S_m, meta_18S_nNA[1:2], by="Station")
meta_all$In.Out <- as.factor(meta_all$In.Out)
meta_all%>%group_by(Fjord2)%>%summarise(min_depth = min(bottom_depth), max_depth = max(bottom_depth))list_meta <- sapply(ls(pattern="meta_"), function(x) get(x), simplify = FALSE)
rm(list = ls(pattern="meta_"))##
## Kruskal-Wallis rank sum test
##
## data: temperature...C. + salinity..psu. + O2umol.l + Fluorometer + PO4_umol.l + NO3_umol.l + Si_umol.l by Bioclimatic_subzone
## Kruskal-Wallis chi-squared = 39.709, df = 3, p-value = 1.228e-08
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/alpha_div/alpha_diversity.R")
#the following is very intense computing so better to not run it too often
euk_r_alpha <- diversity_iNEXT(euk_r)## [1] '3.0.0'prok_alpha <- diversity_iNEXT(prokaryotes)## [1] '3.0.0'euk_r_alpha_meta <- left_join(euk_r_alpha, list_meta$meta_18S_r, by = "Site")
prok_alpha_meta <- left_join(prok_alpha, list_meta$meta_16S, by = "Site")
euk_r_alpha_n <- euk_r_alpha_meta%>%
dplyr::rename(Richness.euk = Richness)%>%
dplyr::rename(Shannon.euk = Shannon)%>%
dplyr::rename(Simpson.euk = Simpson)
prok_alpha_n <- prok_alpha_meta%>%
dplyr::rename(Richness.prok = Richness)%>%
dplyr::rename(Shannon.prok = Shannon)%>%
dplyr::rename(Simpson.prok = Simpson)
euk_r_alpha_n$Pielou.euk <- euk_r_alpha_n$Shannon.euk/log(euk_r_alpha_n$Richness.euk)
prok_alpha_n$Pielou.prok <- prok_alpha_n$Shannon.prok/log(prok_alpha_n$Richness.prok)
euk_r_alpha_meta_l <- euk_r_alpha_n%>%gather(Div_indices, Div_value, Richness.euk, Shannon.euk, Simpson.euk, Pielou.euk)
prok_alpha_meta_l <- prok_alpha_n%>%gather(Div_indices, Div_value, Richness.prok, Shannon.prok, Simpson.prok, Pielou.prok)
div_sub <- c("Station", "Glacial.influence","In.Out","Bioclimatic_subzone", "Div_indices", "Div_value")
#REF 4 pielou: https://www.davidzeleny.net/anadat-r/doku.php/en:div-ind
div_alpha_both <- full_join(euk_r_alpha_meta_l[,div_sub], prok_alpha_meta_l[,div_sub])## Joining, by = c("Station", "Glacial.influence", "In.Out",
## "Bioclimatic_subzone", "Div_indices", "Div_value")list_div <- sapply(ls(pattern="_alpha"), function(x) get(x), simplify = FALSE)
std <- function(x) sd(x)/sqrt(length(x))
list_div$div_alpha_both%>%filter(Div_indices == "Richness.euk")%>%group_by(Bioclimatic_subzone)%>%summarise(median = median(Div_value),min = min(Div_value), max = max(Div_value), std = std(Div_value), n=n())list_div$div_alpha_both%>%filter(Div_indices == "Richness.prok")%>%group_by(Bioclimatic_subzone)%>%summarise(median = median(Div_value),min = min(Div_value), max = max(Div_value), std = std(Div_value), n=n())rm(list = ls(pattern="_alpha"))source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/data_transformations/z_scoring.R")alpha diversity boxplots
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/alpha_div/boxplot_alphadiv.R")## Warning in wilcox.test.default(div_euk_s_G$Richness.euk,
## div_euk_s_NG$Richness.euk, : cannot compute exact p-value with ties
##
## Pairwise comparisons using t tests with pooled SD
##
## data: list_div$euk_r_alpha_n$Richness.euk and list_div$euk_r_alpha_n$Bioclimatic_subzone
##
## high_arctic low_arctic subarctic
## low_arctic 1.0000 - -
## subarctic 0.0027 0.7424 -
## temperate 4.1e-06 5.4e-07 5.3e-13
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: list_div$euk_r_alpha_n$Pielou.euk and list_div$euk_r_alpha_n$Bioclimatic_subzone
##
## high_arctic low_arctic subarctic
## low_arctic 1.00000 - -
## subarctic 0.47591 1.00000 -
## temperate 4e-06 0.00099 0.00072
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: list_div$prok_alpha_n$Richness.prok and list_div$prok_alpha_n$Bioclimatic_subzone
##
## high_arctic low_arctic subarctic
## low_arctic 1.00 - -
## subarctic < 2e-16 7.2e-10 -
## temperate < 2e-16 6.8e-11 0.63
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: list_div$prok_alpha_n$Pielou.prok and list_div$prok_alpha_n$Bioclimatic_subzone
##
## high_arctic low_arctic subarctic
## low_arctic 0.0255 - -
## subarctic 1.0000 0.0028 -
## temperate 1.3e-09 2.2e-11 3.0e-08
##
## P value adjustment method: bonferroni
data transformations
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/data_transformations/transformation_CLR_aitchinson.R")
euk_aitchinson <- dczm(euk_r, 1)## Loading required package: coda.base##
## Attaching package: 'coda.base'## The following object is masked from 'package:stats':
##
## dist## Loading required package: usedist## No. adjusted imputations: 23430prok_aitchinson <- dczm(prokaryotes, 1)## No. adjusted imputations: 21985#prok_aitchinson_r <- dczm(prok_r, 5)
euk_clr <- clr(euk_r,1)
prok_clr <- clr(prokaryotes, 1)
taxonomy_16S_clr <- taxonomy_16S%>%filter(rownames %in% rownames(prok_clr))
taxonomy_16S_clr$ASV <- taxonomy_16S_clr$rownamesmultivariate analysis
RDA
## No. adjusted imputations: 20569## Call: rda(formula = ASV.clr.t ~ temperature...C. + salinity..psu. +
## PO4_umol.l + NO3_umol.l + altitude + bottom_depth + Si_umol.l, data =
## meta)
##
## Inertia Proportion Rank
## Total 2593.3079 1.0000
## Constrained 1139.6719 0.4395 7
## Unconstrained 1453.6360 0.5605 85
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7
## 707.3 166.1 129.7 66.1 39.6 20.0 10.8
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 390.8 148.7 92.6 79.0 76.9 61.8 49.0 39.3
## (Showing 8 of 85 unconstrained eigenvalues)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine## Call: rda(formula = ASV.clr.t.sort ~ temperature...C. + salinity..psu.
## + PO4_umol.l + NO3_umol.l + bottom_depth + altitude, data =
## meta.wf.data)
##
## Inertia Proportion Rank
## Total 2593.3079 1.0000
## Constrained 1105.9357 0.4265 6
## Unconstrained 1487.3723 0.5735 86
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## 706.6 161.7 129.7 64.1 32.4 11.3
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 396.8 150.3 95.1 82.8 76.9 63.3 50.9 42.7
## (Showing 8 of 86 unconstrained eigenvalues)
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = ASV.clr.t.sort ~ temperature...C. + salinity..psu. + PO4_umol.l + NO3_umol.l + bottom_depth + altitude, data = meta.wf.data)
## Df Variance F Pr(>F)
## Model 6 1105.9 10.658 0.001 ***
## Residual 86 1487.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.4264575
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Bioclimatic_subzone, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Bioclimatic_subzone 3 130524 0.43619 22.951 0.001 ***
## Residual 89 168716 0.56381
## Total 92 299240 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Bioclimatic_subzone)
##
## No. of Positive Eigenvalues: 61
## No. of Negative Eigenvalues: 31
##
## Average distance to median:
## high_arctic low_arctic subarctic temperate
## 0.07048 0.05165 0.11178 0.10086
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 92 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 1.52249 0.86461 0.22034 0.09360 0.06507 0.05557 0.04519 0.02832
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Region, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Region 5 146245 0.48872 16.632 0.001 ***
## Residual 87 152995 0.51128
## Total 92 299240 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Region)
##
## No. of Positive Eigenvalues: 61
## No. of Negative Eigenvalues: 31
##
## Average distance to median:
## East.Greenland Iceland North.Norway South.Norway Svalbard
## 0.03183 0.03631 0.10196 0.10086 0.07048
## West.Greenland
## 0.05670
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 92 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 1.52249 0.86461 0.22034 0.09360 0.06507 0.05557 0.04519 0.02832
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Fjord, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Fjord 17 215275 0.7194 11.311 0.001 ***
## Residual 75 83965 0.2806
## Total 92 299240 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Fjord)
##
## No. of Positive Eigenvalues: 61
## No. of Negative Eigenvalues: 31
##
## Average distance to median:
## Balsfjord Boknafjord
## 0.03706 0.05537
## Disco Bay Iceland
## 0.05670 0.03631
## Isfjord Kongsfjord
## 0.01447 0.08791
## Laksefjord Lofoten
## 0.02932 0.10601
## Lyngenfjord Nordfjord
## 0.02967 0.04756
## Nordvestfjord.Scoresby.Sund Orust-Tjˆrn.Fjord
## 0.03183 0.05487
## Porsangerfjord Sognefjord
## 0.03184 0.04248
## Tanafjord Van.Mijen.Fjord
## 0.06507 0.04194
## Wijdefjord Woodfjord
## 0.03149 0.03019
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 92 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 1.52249 0.86461 0.22034 0.09360 0.06507 0.05557 0.04519 0.02832## No. adjusted imputations: 19453## Call: rda(formula = ASV.clr.t ~ Temperature...C. + Salinity..psu. +
## bottom_depth + NO3_umol.l + altitude + MLD + PO4_umol.l + Si_umol.l,
## data = meta)
##
## Inertia Proportion Rank
## Total 3192.1962 1.0000
## Constrained 977.8773 0.3063 8
## Unconstrained 2214.3189 0.6937 81
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8
## 460.5 179.3 124.4 78.8 52.2 32.7 31.7 18.1
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 350.1 225.5 147.9 133.8 104.4 87.3 80.4 70.5
## (Showing 8 of 81 unconstrained eigenvalues)## Call: rda(formula = ASV.clr.t.sort ~ Temperature...C. + Salinity..psu.
## + Fluorometer + PO4_umol.l + Si_umol.l + NO3_umol.l + bottom_depth +
## altitude, data = meta.wf.data)
##
## Inertia Proportion Rank
## Total 3192.1962 1.0000
## Constrained 969.0544 0.3036 8
## Unconstrained 2223.1418 0.6964 81
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8
## 461.3 168.8 124.7 77.0 47.8 39.8 29.8 19.8
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 367.4 226.6 147.5 133.8 100.9 89.7 81.1 68.9
## (Showing 8 of 81 unconstrained eigenvalues)
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = ASV.clr.t.sort ~ Temperature...C. + Salinity..psu. + Fluorometer + PO4_umol.l + Si_umol.l + NO3_umol.l + bottom_depth + altitude, data = meta.wf.data)
## Df Variance F Pr(>F)
## Model 8 969.05 4.4134 0.001 ***
## Residual 81 2223.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] 0.3035698
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Bioclimatic_subzone, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Bioclimatic_subzone 3 0.64750 0.44004 22.527 0.001 ***
## Residual 86 0.82397 0.55996
## Total 89 1.47148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Bioclimatic_subzone)
##
## No. of Positive Eigenvalues: 62
## No. of Negative Eigenvalues: 27
##
## Average distance to median:
## high_arctic low_arctic subarctic temperate
## 0.09487 0.09335 0.08179 0.08659
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 89 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 0.69767 0.31063 0.16793 0.07851 0.03762 0.03701 0.02817 0.01852
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Region, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Region 5 0.81254 0.5522 20.716 0.001 ***
## Residual 84 0.65893 0.4478
## Total 89 1.47148 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Region)
##
## No. of Positive Eigenvalues: 62
## No. of Negative Eigenvalues: 27
##
## Average distance to median:
## East.Greenland Iceland North.Norway South.Norway Svalbard
## 0.03000 0.03014 0.06956 0.08659 0.09487
## West.Greenland
## 0.04299
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 89 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 0.69767 0.31063 0.16793 0.07851 0.03762 0.03701 0.02817 0.01852
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = ASV.ait.t ~ Fjord, data = meta, method = "jaccard")
## Df SumOfSqs R2 F Pr(>F)
## Fjord 17 1.0801 0.73401 11.687 0.001 ***
## Residual 72 0.3914 0.26599
## Total 89 1.4715 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dis, group = meta$Fjord)
##
## No. of Positive Eigenvalues: 62
## No. of Negative Eigenvalues: 27
##
## Average distance to median:
## Balsfjord Boknafjord
## 0.02819 0.03297
## Disco Bay Iceland
## 0.04299 0.03014
## Isfjord Kongsfjord
## 0.08400 0.08301
## Laksefjord Lofoten
## 0.02429 0.07719
## Lyngenfjord Nordfjord
## 0.03329 0.04437
## Nordvestfjord.Scoresby.Sund Orust-Tjˆrn.Fjord
## 0.03000 0.05833
## Porsangerfjord Sognefjord
## 0.04787 0.09209
## Tanafjord Van.Mijen.Fjord
## 0.03526 0.05503
## Wijdefjord Woodfjord
## 0.04599 0.05647
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 89 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 0.69767 0.31063 0.16793 0.07851 0.03762 0.03701 0.02817 0.01852distance analysis
## Loading required package: metagMisc##
## Attaching package: 'metagMisc'## The following object is masked from 'package:purrr':
##
## some## Loading required package: kmed##
## Attaching package: 'kmed'## The following object is masked from 'package:survival':
##
## heart## Loading required package: energy## No. adjusted imputations: 1593
## [1] 45
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 2.7584, df = 43, p-value = 0.008497
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1063037 0.6116572
## sample estimates:
## cor
## 0.3877387
## No. adjusted imputations: 1593
## [1] 77
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 7.6598, df = 75, p-value = 5.286e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5149482 0.7719449
## sample estimates:
## cor
## 0.6625143
## No. adjusted imputations: 1593
## [1] 112
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 7.2939, df = 110, p-value = 4.93e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4310631 0.6840374
## sample estimates:
## cor
## 0.5709507
## No. adjusted imputations: 1593
## [1] 150
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 5.7104, df = 148, p-value = 5.974e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2839851 0.5478623
## sample estimates:
## cor
## 0.4249083
## No. adjusted imputations: 1593
## [1] 164
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 7.044, df = 162, p-value = 5.061e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3574980 0.5934325
## sample estimates:
## cor
## 0.4842192
## No. adjusted imputations: 1700
## [1] 45
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 0.46145, df = 43, p-value = 0.6468
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2280361 0.3563870
## sample estimates:
## cor
## 0.07019651
## No. adjusted imputations: 1700
## [1] 77
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 3.9395, df = 75, p-value = 0.0001815
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2095156 0.5838915
## sample estimates:
## cor
## 0.4140636
## No. adjusted imputations: 1700
## [1] 112
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 6.4767, df = 110, p-value = 2.714e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3765772 0.6478161
## sample estimates:
## cor
## 0.5254188
## No. adjusted imputations: 1700
## [1] 150
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 6.3825, df = 148, p-value = 2.114e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3288029 0.5815460
## sample estimates:
## cor
## 0.4645834
## No. adjusted imputations: 1700
## [1] 164
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: all_16S_c$particle_normalized and all_16S_c$value.x
## t = 2.6516, df = 162, p-value = 0.008808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05233421 0.34637239
## sample estimates:
## cor
## 0.2039484
## Loading required package: raster## Loading required package: sp##
## Attaching package: 'sp'## The following object is masked from 'package:coda.base':
##
## coordinates##
## Attaching package: 'raster'## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:dplyr':
##
## select## Loading required package: viridisLite## No. adjusted imputations: 20569
##
## Call:
## lm(formula = V1 ~ value, data = SETA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6320 -0.9716 0.0963 1.0560 4.2465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.307642 0.361216 -20.23 <2e-16 ***
## value 0.150038 0.004127 36.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.645 on 304 degrees of freedom
## Multiple R-squared: 0.813, Adjusted R-squared: 0.8124
## F-statistic: 1321 on 1 and 304 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = V1 ~ value, data = SETB)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9587 -0.7204 -0.1989 0.8046 5.6794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.124755 0.455671 -6.857 1.81e-10 ***
## value 0.089145 0.006569 13.570 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 147 degrees of freedom
## Multiple R-squared: 0.5561, Adjusted R-squared: 0.5531
## F-statistic: 184.1 on 1 and 147 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = V1 ~ value, data = SETC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2852 -1.1285 -0.2510 0.5407 5.9446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.72559 0.87060 -0.833 0.40931
## value 0.05004 0.01788 2.799 0.00771 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 42 degrees of freedom
## Multiple R-squared: 0.1572, Adjusted R-squared: 0.1372
## F-statistic: 7.835 on 1 and 42 DF, p-value: 0.007707## `geom_smooth()` using formula = 'y ~ x'
## No. adjusted imputations: 19453
##
## Call:
## lm(formula = V1 ~ value, data = SETA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4182 -1.8609 -0.6411 1.5712 9.5038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.59194 1.01983 -4.503 9.63e-06 ***
## value 0.12010 0.01211 9.915 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.246 on 299 degrees of freedom
## Multiple R-squared: 0.2474, Adjusted R-squared: 0.2449
## F-statistic: 98.31 on 1 and 299 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = V1 ~ value, data = SETB)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0621 -1.0204 0.1027 1.0222 6.4127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.79208 0.67834 -5.590 1.06e-07 ***
## value 0.08754 0.00891 9.826 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.571 on 148 degrees of freedom
## Multiple R-squared: 0.3948, Adjusted R-squared: 0.3907
## F-statistic: 96.54 on 1 and 148 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = V1 ~ value, data = SETC)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9804 -0.8702 -0.1710 0.3455 6.0828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19817 0.80714 -0.246 0.8071
## value 0.02608 0.01028 2.538 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.528 on 49 degrees of freedom
## Multiple R-squared: 0.1162, Adjusted R-squared: 0.09815
## F-statistic: 6.442 on 1 and 49 DF, p-value: 0.01438## `geom_smooth()` using formula = 'y ~ x'
functional co-analysis of prokaryotes and picoeukaryotes and their trophic groups
source("/Users/corahoerstmann/Documents/AWI_ArcticFjords/Code/analysis_network/top_abundant.R")## Loading required package: NetCoMi## Loading required package: SpiecEasi##
## Attaching package: 'SpiecEasi'## The following object is masked _by_ '.GlobalEnv':
##
## clr## The following object is masked from 'package:MASS':
##
## fitdistr## ## Loading required package: igraph##
## Attaching package: 'igraph'## The following object is masked from 'package:SpiecEasi':
##
## make_graph## The following object is masked from 'package:raster':
##
## union## The following object is masked from 'package:BBmisc':
##
## normalize## The following object is masked from 'package:vegan':
##
## diversity## The following object is masked from 'package:permute':
##
## permute## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:purrr':
##
## compose, simplify## The following object is masked from 'package:tidyr':
##
## crossing## The following object is masked from 'package:tibble':
##
## as_data_frame## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## union## Loading required package: qgraph## Loading required package: limma##
## Attaching package: 'limma'## The following object is masked from 'package:BBmisc':
##
## printHead## Loading required package: fantaxtic## Warning in Bioclimatic_subzone == c("high_arctic", "low_arctic"): longer object
## length is not a multiple of shorter object length## Bioclimatic_subzone_B tax_rank taxid abundance Tax_1.x
## 1 Arctic 5 ASV3 2.295080e+12 Bacteria
## 2 Arctic 10 ASV11 1.432178e+12 Bacteria
## 3 Arctic 7 ASV12 1.591738e+12 Bacteria
## 4 Arctic 1 ASV14 7.017594e+12 Bacteria
## 5 Temperate 9 ASV41 4.155330e+12 Bacteria
## 6 Subarctic 6 ASV86 3.459788e+12 Bacteria
## 7 Temperate 5 ASV86 6.014654e+12 Bacteria
## 8 Temperate 7 ASV101 5.397365e+12 Bacteria
## 9 Subarctic 2 ASV168 8.671690e+12 Bacteria
## 10 Temperate 4 ASV168 6.129200e+12 Bacteria
## 11 Subarctic 8 ASV185 3.154669e+12 Bacteria
## 12 Subarctic 10 ASV197 2.992544e+12 Bacteria
## 13 Temperate 6 ASV207 5.474159e+12 Bacteria
## 14 Subarctic 3 ASV397 8.102563e+12 Bacteria
## 15 Temperate 8 ASV397 4.221519e+12 Bacteria
## 16 Subarctic 7 ASV444 3.244443e+12 Bacteria
## 17 Temperate 10 ASV603 3.933225e+12 Bacteria
## 18 Arctic 4 >ASV_6 2.534052e+12 Eukaryota
## 19 Subarctic 5 >ASV_8 3.571373e+12 Eukaryota
## 20 Temperate 1 >ASV_8 1.741606e+13 Eukaryota
## 21 Arctic 3 >ASV_17 5.029885e+12 Eukaryota
## 22 Arctic 9 >ASV_23 1.464549e+12 Eukaryota
## 23 Arctic 8 >ASV_39 1.509809e+12 Eukaryota
## 24 Arctic 2 >ASV_122 5.530982e+12 Eukaryota
## 25 Subarctic 4 >ASV_122 5.501693e+12 Eukaryota
## 26 Temperate 3 >ASV_122 7.498668e+12 Eukaryota
## 27 Subarctic 9 >ASV_147 3.116342e+12 Eukaryota
## 28 Subarctic 1 >ASV_155 3.185160e+13 Eukaryota
## 29 Temperate 2 >ASV_253 9.191767e+12 Eukaryota
## 30 Arctic 6 >ASV_1290 1.826876e+12 Eukaryota
## Tax_2.x Tax_3.x Tax_4.x
## 1 Proteobacteria Alphaproteobacteria Rhodobacterales
## 2 Proteobacteria Gammaproteobacteria Cellvibrionales
## 3 Proteobacteria Gammaproteobacteria SAR86 clade
## 4 Bacteroidetes Bacteroidia Flavobacteriales
## 5 Proteobacteria Gammaproteobacteria Vibrionales
## 6 Bacteroidetes Bacteroidia Flavobacteriales
## 7 Bacteroidetes Bacteroidia Flavobacteriales
## 8 Bacteroidetes Bacteroidia Flavobacteriales
## 9 Proteobacteria Gammaproteobacteria Betaproteobacteriales
## 10 Proteobacteria Gammaproteobacteria Betaproteobacteriales
## 11 Proteobacteria Deltaproteobacteria Bdellovibrionales
## 12 Bacteroidetes Bacteroidia Flavobacteriales
## 13 Proteobacteria Gammaproteobacteria Alteromonadales
## 14 Verrucomicrobia Verrucomicrobiae Opitutales
## 15 Verrucomicrobia Verrucomicrobiae Opitutales
## 16 Planctomycetes Planctomycetacia Pirellulales
## 17 Proteobacteria Deltaproteobacteria Bdellovibrionales
## 18 Archaeplastida Chlorophyta Mamiellophyceae
## 19 Stramenopiles Ochrophyta Bacillariophyta
## 20 Stramenopiles Ochrophyta Bacillariophyta
## 21 Alveolata Dinoflagellata Dinophyceae
## 22 Stramenopiles Ochrophyta Bolidophyceae
## 23 Stramenopiles <NA> <NA>
## 24 Stramenopiles Ochrophyta Chrysophyceae
## 25 Stramenopiles Ochrophyta Chrysophyceae
## 26 Stramenopiles Ochrophyta Chrysophyceae
## 27 Alveolata Dinoflagellata <NA>
## 28 Alveolata Dinoflagellata Syndiniales
## 29 <NA> <NA> <NA>
## 30 Opisthokonta Choanoflagellida Choanoflagellatea
## Tax_5.x Tax_6.x Tax_1.y Tax_2.y Tax_3.y
## 1 Rhodobacteraceae <NA> Bacteria Proteobacteria Alphaproteobacteria
## 2 Porticoccaceae <NA> Bacteria Proteobacteria Gammaproteobacteria
## 3 g__ <NA> Bacteria Proteobacteria Gammaproteobacteria
## 4 Flavobacteriaceae <NA> Bacteria Bacteroidetes Bacteroidia
## 5 Vibrionaceae <NA> Bacteria Proteobacteria Gammaproteobacteria
## 6 Crocinitomicaceae <NA> Bacteria Bacteroidetes Bacteroidia
## 7 Crocinitomicaceae <NA> Bacteria Bacteroidetes Bacteroidia
## 8 NS9 marine group <NA> Bacteria Bacteroidetes Bacteroidia
## 9 Burkholderiaceae <NA> Bacteria Proteobacteria Gammaproteobacteria
## 10 Burkholderiaceae <NA> Bacteria Proteobacteria Gammaproteobacteria
## 11 Bacteriovoracaceae <NA> Bacteria Proteobacteria Deltaproteobacteria
## 12 NS7 marine group <NA> Bacteria Bacteroidetes Bacteroidia
## 13 Colwelliaceae <NA> Bacteria Proteobacteria Gammaproteobacteria
## 14 Puniceicoccaceae <NA> Bacteria Verrucomicrobia Verrucomicrobiae
## 15 Puniceicoccaceae <NA> Bacteria Verrucomicrobia Verrucomicrobiae
## 16 Pirellulaceae <NA> Bacteria Planctomycetes Planctomycetacia
## 17 Bdellovibrionaceae <NA> Bacteria Proteobacteria Deltaproteobacteria
## 18 Mamiellales <NA> Eukaryota Archaeplastida Chlorophyta
## 19 Bacillariophyta_X <NA> Eukaryota Stramenopiles Ochrophyta
## 20 Bacillariophyta_X <NA> Eukaryota Stramenopiles Ochrophyta
## 21 Dinophyceae_X <NA> Eukaryota Alveolata Dinoflagellata
## 22 Parmales <NA> Eukaryota Stramenopiles Ochrophyta
## 23 g__ <NA> Eukaryota Stramenopiles <NA>
## 24 Chrysophyceae_X <NA> Eukaryota Stramenopiles Ochrophyta
## 25 Chrysophyceae_X <NA> Eukaryota Stramenopiles Ochrophyta
## 26 Chrysophyceae_X <NA> Eukaryota Stramenopiles Ochrophyta
## 27 g__ <NA> Eukaryota Alveolata Dinoflagellata
## 28 Dino-Group-II <NA> Eukaryota Alveolata Dinoflagellata
## 29 g__ <NA> Eukaryota <NA> <NA>
## 30 Choanoflagellatea_X <NA> Eukaryota Opisthokonta Choanoflagellida
## Tax_4.y Tax_5.y Tax_6.y
## 1 Rhodobacterales Rhodobacteraceae Amylibacter
## 2 Cellvibrionales Porticoccaceae SAR92 clade
## 3 SAR86 clade g__ f__
## 4 Flavobacteriales Flavobacteriaceae NS4 marine group
## 5 Vibrionales Vibrionaceae Vibrio
## 6 Flavobacteriales Crocinitomicaceae Fluviicola
## 7 Flavobacteriales Crocinitomicaceae Fluviicola
## 8 Flavobacteriales NS9 marine group f__
## 9 Betaproteobacteriales Burkholderiaceae RS62 marine group
## 10 Betaproteobacteriales Burkholderiaceae RS62 marine group
## 11 Bdellovibrionales Bacteriovoracaceae Peredibacter
## 12 Flavobacteriales NS7 marine group f__
## 13 Alteromonadales Colwelliaceae Colwellia
## 14 Opitutales Puniceicoccaceae MB11C04 marine group
## 15 Opitutales Puniceicoccaceae MB11C04 marine group
## 16 Pirellulales Pirellulaceae Blastopirellula
## 17 Bdellovibrionales Bdellovibrionaceae OM27 clade
## 18 Mamiellophyceae Mamiellales Mamiellaceae
## 19 Bacillariophyta Bacillariophyta_X Polar-centric-Mediophyceae
## 20 Bacillariophyta Bacillariophyta_X Polar-centric-Mediophyceae
## 21 Dinophyceae Dinophyceae_X Dinophyceae_XX
## 22 Bolidophyceae Parmales Parmales_X
## 23 <NA> g__ f__
## 24 Chrysophyceae Chrysophyceae_X Chrysophyceae_Clade-F
## 25 Chrysophyceae Chrysophyceae_X Chrysophyceae_Clade-F
## 26 Chrysophyceae Chrysophyceae_X Chrysophyceae_Clade-F
## 27 <NA> g__ f__
## 28 Syndiniales Dino-Group-II Dino-Group-II-Clade-16
## 29 <NA> g__ f__
## 30 Choanoflagellatea Choanoflagellatea_X Choanoflagellatea_X_Group_L
## trophy X
## 1 het
## 2 het
## 3 het
## 4 het
## 5 het
## 6 het
## 7 het
## 8 het
## 9 het
## 10 het
## 11 het
## 12 het
## 13 het
## 14 het
## 15 het
## 16 het
## 17 het
## 18 auto
## 19 auto
## 20 auto
## 21 unknown
## 22 auto
## 23 unknown
## 24 mixo
## 25 mixo
## 26 mixo
## 27 unknown
## 28 het
## 29 unknown
## 30 het## `summarise()` has grouped output by 'Sample'. You can override using the
## `.groups` argument.
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.0006399 3.2e-04 29.04 1.66e-09 ***
## Residuals 59 0.0006500 1.1e-05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.02637 0.013184 16.22 2.43e-06 ***
## Residuals 59 0.04795 0.000813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.006317 0.0031584 27.59 3.48e-09 ***
## Residuals 59 0.006755 0.0001145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.6203 0.31015 32.73 2.74e-10 ***
## Residuals 59 0.5591 0.00948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.006982 0.003491 32.62 2.88e-10 ***
## Residuals 59 0.006314 0.000107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Bioclimatic_subzone_B 2 0.0006399 3.2e-04 29.04 1.66e-09 ***
## Residuals 59 0.0006500 1.1e-05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Bacteria auto` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 0.0021 -
## Temperate 1.5e-09 4.7e-06
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Eukaryota auto` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 1.2e-06 -
## Temperate 0.0011 0.3151
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Eukaryota mixo` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 1.6e-09 -
## Temperate 9.3e-05 0.029
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Bacteria het` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 3e-10 -
## Temperate 0.00172 0.00023
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Archaea het` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 1.1e-08 -
## Temperate 0.49 3.2e-07
##
## P value adjustment method: bonferroni
##
## Pairwise comparisons using t tests with pooled SD
##
## data: sTATS2$`Eukaryota het` and sTATS2$Bioclimatic_subzone_B
##
## Arctic Subarctic
## Subarctic 6.6e-09 -
## Temperate 0.13 4.6e-06
##
## P value adjustment method: bonferroni